30Years of Multidimensional Multivariate Visualization
Pak Chung Wong R.Daniel Bergeron
pcw@https://www.doczj.com/doc/364366840.html, rdb@https://www.doczj.com/doc/364366840.html,
Department of Computer Science
University of New Hampshire
Durham,New Hampshire03824,USA
Abstract
We present a survey of multidimensional multivariate(mdmv)visualization techniques developed during the last three decades.This sub?eld of scienti?c visualization deals with the analysis of data with multiple
parameters or factors,and the key relationships among them.The course of development is roughly organized
into four stages,within which major milestones are discussed.Recently developed techniques are explored
with examples.
1Introduction
Multidimensional multivariate visualization is an important sub?eld of scienti?c visualization.It was studied sep-arately by statisticians and psychologists long before computer science was deemed a discipline.The appearance of low-priced personal computers and workstations during the1980’s breathed new life into graphical analysis of mdmv data.This research topic was among one of the short-term goals included in the1987National Science Foundation(NSF)sponsored workshop on Visualization in Scienti?c Computing[MDB87].The quest for effective and ef?cient mdmv visualization techniques has expanded since then.
This paper attempts to trace three decades of intensive development in this visualization?eld.It is by no means a comprehensive survey.We provide a brief history along with a description of the principal concepts of some mdmv visualization techniques.Recently developed mdmv visualization techniques are discussed in detail with examples.A remark of the trends of mdmv visualization research is given.
2Four Stages of Multidimensional Multivariate Visualization Develop-ment
The last three decades of mdmv visualization development can be roughly characterized into four stages.The classic exploratory data analysis(EDA)book by Tukey[Tuk77],the1987NSF workshop on Visualization in
Scienti?c Computing[MDB87],and the IEEE Visualization’91conference[NR91]are the watersheds de?ning these stages.The?rst stage was primarily concerned with the graphical presentation of either one or two variate data.The second stage was dominated by Tukey’s exploratory data analysis.Scientists started looking at graphical data with a different perspective.Although most of the graphics was still two dimensional,scientists were able to encode data with multiple parameters,i.e.,multivariate,into meaningful two dimensional plots.The momentum of this work carried on through the next stage when NSF recognized the importance of mdmv data visualization.The involvement of computer scientists accelerated the growth of the research by computerizing many of the old ideas and developing many new ones.The mission was formally de?ned and many promising concepts were developed during the following few years.The?nal(current)stage is concerned with the elaboration and assessment of mdmv visualization techniques.It remains to be seen whether the existing mdmv visualization concepts can lead to better visualization of a problem and better understanding of the underlying science.This discussion of mdmv visualization is far from complete.There are other important topics including volume visualization and vector/tensor?eld visualization that are not covered.The principal concepts and research issues related to these subjects can be found in[Nie92,PvW92,KHK94,HPvW94].
2.1Pre–1976The Searching Stage
Scientists have studied multivariate visualization since1782when Crome used point symbols to show the geo-graphical distribution in Europe of56commodities[Col93].In1950,Gibson[Gib50]started the research on visual texture https://www.doczj.com/doc/364366840.html,ter,Pickett and White[PW66]proposed mapping data sets onto arti?cial graphical objects composed of lines.This texture mapping work was further investigated by Pickett[Pic70],and was eventually computerized[PG88].Chernoff[Che73]presented his arrays of cartoon faces for multivariate data in1973.In this well-known technique,variates are mapped to the shape of the cartoon faces and their facial features including nose,mouth,and eyes.These faces are then displayed in a two dimensional graph.
The searching stage can be characterized by relatively small sized data,and tools for data visualization that usually consisted of color pencils and graph paper.The graphical output was mostly two dimensional-displays. Statisticians were the dominant research force during this period.Graphics was used to bring out the key features of the data,suggest statistical analysis methods that are applied to the data,and present the conclusions[Fis70].
2.21977–1985The Awakening Stage
Tukey’s exploratory data analysis signi?ed a new era of scienti?c data visualization.Exploratory data analysis is more than a tool;it is a way of thinking.It teaches people how to visually decode information from the data.When the personal computer arrived,it became the scientist’s most powerful tool ever.Now scientists could visualize data beyond two dimensions interactively.The painfully long calculations suddenly became available in real time. Statisticians could visualize data during each stage of the analysis instead of waiting until the?nal results were available.The availability of other computer hardware such as high resolution color displays also gave the study of mdmv visualization new opportunities.
During this stage,two and three dimensional spatial data were the most common data types being studied,
although multivariate data started gaining more attention.Asimov[Asi85]presented the grand tour technique for viewing projections of multivariate data on two dimensional planes.Earth resource satellites sent out decades ago are still transmitting data continuously.Gigabyte sized multivariate data had arrived.
2.31986–1991The Discovery Stage
The1987NSF workshop formally declared the need for two and three dimensional spatial object visualization.The two dimensional projections of multivariate data sets is also included as one of the short-term potential targets for scienti?c visualization research.Once the mission was de?ned,scientists started pushing hard on the representation and visualization of mdmv data.The limited availability of high speed graphics hardware during the previous stage was gradually conquered.A majority of research was directed away from the development of exploratory data analysis tools,which lay heavily on statistical measures,towards colorful high dimensional graphics that required high speed computations.Some of the mdmv visualization concepts developed during this stage include:grand tour methods[BA86],parallel coordinates[IRC87,ID87,ID90],iconography[PG88,BG89b,Bed90,Lev91],worlds within worlds[FB90a,FB90b],dimension stacking[LWW90],hierarchical axis[MGTS90,MTS91a,MTS91b], hyperbox[AC91],and various ideas collected in[Cle93,CMM93].Some of these techniques attempt to show all dimensions and all variates visually as one display,whereas others aim at direct manipulation graphics,in which the user interactively selects subsets for display by using an input device such as a mouse.Virtual reality [FB90a,FB90b]began to appear in the mdmv visualization literature.
2.41992–present The Elaboration and Assessment Stage
In1990and1991,there were at least fourteen mdmv related papers published in the IEEE Visualization conferences.
A total of four have been published in the three visualization conferences since then.This stage so far has been a period of retrenchment in the development of new mdmv visualization techniques.Some of the most recently developed tools are,each in a different way,elaborations of work done in previous stages.For example,HyperSlice [vWvL93]is an attempt to combine the panel matrix of scatterplot matrix with direct manipulation of brushing [BC87].AutoVisual[BF92,BF93]is an extended version of worlds within worlds with a new rule-based interfaces.XmdvTool[War94]integrates four existing mdmv visualization tools:dimension stacking,scatterplot matrix,glyphs,and parallel coordinates into one system with enhanced n-dimensional brushing.
The research in mdmv visualization has also been diversi?ed into multidisciplinary collaborations.Attempts to combine sound with graphics[SPW92,SBG92]are currently being made.The concept of a rule-based queue [BF92,BF93]was also introduced.One of the latest research issues of mdmv visualization is the need to evaluate the correctness,effectiveness,and usefulness of mdmv visualization techniques.Similar concerns also appear in the other?elds of visualization research[RET94,HPvW94].
3Terminology
Unfortunately,the mdmv literature suffers from ill-de?ned and inconsistent terminology.The term dimensionality is especially overloaded.Mathematicians consider dimension as the number of independent variables in an algebraic equation.Engineers take dimension as measurements of any sort(breadth,length,height,and thickness). Even the pre?x multi is frequently interchanged with another pre?x hyper.In statistics literatures,the pre?x multi means two or more,indicating a natural breakpoint between one and two dimension in probabilistic methods.For the breakpoint between three and four(or beyond),the pre?x hyper is used[Cle93].We use the pre?x multi to refer to dimensionality of two or more.
Beddow[Bed92]points out the difference between multidimensional objects and multidimensional data. Multidimensional objects are spatial objects,and our goal is to understand their geometry.The most common form are two dimensional images and three dimensional volumes.They can best be described as n-dimensional Euclidean spaces.Multidimensional data,on the other hand,refers to the study of relationships among multiple parameters.Mathematically these parameters can be classi?ed into two categories:dependent and independent [KK93].Some statisticians prefer the terms factor and response[Cle93].A variable is said to be dependent if it is a function of another variable,the independent variable.The relationship of an independent variable and a dependent variable can best be described by the mathematical equation.We adopt the convention that the term multidimensional refers to the dimensionality of the independent variables,while the term multivariate refers to the dimensionality of the dependent variables[BCH94].This is by far the most popular way to describe the dimensionality of mdmv data sets in scienti?c visualization literature.For example,a three dimensional volume space in which both temperature and pressure are observed and recorded in various locations produces 3d2v data.
Beddow[Bed92]argues that analytic methods used to explore n-dimensional Euclidean spaces are not appropriate for general multivariate analysis.In mdmv visualization research,the emphasis shifts away from the strong mathematical de?nition of dependent and independent variates towards the broader de?nition of multiple variables or factors.This happens not only in mdmv scienti?c visualization research but also in statistical studies. The tools are different,but the goal is the same:to?nd the hidden relationships between the variables(also known as?tting in statistics).
In general,raw scienti?c data can be categorized into a hierarchy of data types.The most general and the lowest of the hierarchy is the nominal data,whose values have no inherent ordering.For example,the names of the?fty states are nominal data.The next higher type of the hierarchy is ordinal data,whose values are ordered, but for which no meaningful distance metric exists.The seven rainbow colors(i.e.,red,orange,)belong to this category.The highest of the hierarchy is metric data,which has a meaningful distance metric between any two values.Times,distances,and temperatures are examples.If we bin metric data into ranges,it becomes ordinal data.If we further remove the ordering constraints,the data is nominal.Some of the visualization techniques included in this survey are specially designed to handle metric data(see Sections5.2.2and5.2.9.) The above3d2v temperature/pressure example more or less implies that each3dimensional coordinates contain simple(i.e.,neither a set nor an interval)and atomic(i.e.,not composite)values of pressure and temperatures.This is different from the case when we measure,for example,the chemical contents of a volume.Each coordinates
now has a set(instead of a simple value)of composite data(i.e.,chemical elements.)The varying numbers of values of a variate plotted in any single dimensional point is known as the density of that coordinate.
4Fundamental Objective and Approach
The main objectives of mdmv visualization are to visually summarize an mdmv data set,and?nd key trends and relationships among the variates.Different properties and characteristics of the data may changes the way we carry out visualization,but not its goals.
The traditional two dimensional point and line plots are among the most commonly used visualization tech-niques for data with lower number of variates.This technique can be enhanced by putting an array of plots into one display,so as to add another variate to the visual presentation.This approach is discussed in Sections5.1.5 and5.2.2.
We can also map the variates of the data into graphical primitives of differnt colors,sizes,shapes,and locations (see Sections5.2.4,5.2.5,and5.2.6.)The display of all dimensions and all variates creates some kind of texture patterns,and provide critical insights needed for scienti?c discovery.
For large(larger than the number of pixels of a display)scienti?c data,we can display a certain portion of data and allow the user to navigate the rest interactively.This is described in Sections5.2.2,5.2.7,5.2.8,and5.2.9, Most of the visualization techniques assume a Euclidean space environment.Orthogonal axes,however,are not always the best choice to plot data.Sections5.2.7,5.2.10,and5.2.11give some alternatives.
A powerful visualization technique is to display the data frame by frame according to a time variate.This animation approach is discussed in Sections5.3.1,5.3.2,and5.3.3.
5Multidimensional Multivariate Visualization and Concepts
The body of this paper covers the principal concepts and brief history of some of the popular mdmv visualization techniques.During the last decade,hundreds of so-called new mdmv visualization techniques have been invented. (Refer to[KK93]for more details in this regard.)A majority of them are designed for special purposes such as volume visualization and vector/tensor?eld visualization,which are not covered in our discussion.Some of the rest are merely ad hoc tools that produce pretty pictures.They are dif?cult to create and their results are hard to interpret.We are interested in techniques that are founded on a solid basis and that have potential for practical value.
Categorizing mdmv visualization techniques is a dif?cult task.Possible criteria for such a categorization include the goal of the visualization,the type and/or dimensionality of the data,the dimensionality of the visualization technique,etc.We have not found a convincing set of criteria that cleanly differentiate the visualization techniques we wish to describe.We have chosen to group the techniques into those based on2-variate displays,those based on multivariate displays,and those using time as an animation parameter:
Techniques based on2-variate displays include the fundamental2-variate displays and simultaneous views
of2-variate displays.Most of the them developed in the statistics world.Both visual perception and statistical?tting of the data are of major concern.The data size is relatively small,usually in the order of hundreds of items.The graphics are mostly variations on two dimensional point and line plots.
Multivariate display are the basis for many recently developed mdmv techniques,most of which use colorful graphics created by high-speed graphics computation.The data is usually larger and more complicated.A majority of the techniques were developed within the period of1987–1991.
Animation is a powerful tool for visualizing mdmv scienti?c data.V arious movie animation techniques on mdmv data,and a scalar visualization animation model are presented.In principle,any single frame visualization technique can be extended to animation if the data can be represented as a time series showing two-way correlations.
5.1Techniques Based on2-variate Displays
This section highlights some of the tools and summarizes the general approach developed based on2-variate displays.The discussion is based upon the book by Cleveland[Cle93],which has a good collection of elegant visualization techniques developed by Cleveland,Tukey,and others throughout the80’s.Tukey’s exploratory data analysis[Tuk77]is an important milestone of data visualization;most of the techniques were developed with pencil and paper during the early70’s.Cleveland’s work emphasizes the structure of data and the validity of statistical models?tted to data.A majority of the visualization techniques are two dimensional,with the exception of isosurface plotting.Color is rarely used.Most of the tools show correlations between two variates.Our discussion skips the formulas,algorithms,and theories;only the concepts and techniques are presented.
5.1.1Data Types
The basic data types for statistical data analysis are univariate,bivariate,trivariate,and hypervariate which represent data with one dimension and one,two,three,and four or more variates.Cleveland also describes the multiway data type for data with higher dimensionality.
5.1.2Reference Grids
The most common display unit in statistics visualization is a two dimensional scatterplot,as depicted in the left panel of Figure1.In the middle panel,simple grid lines are drawn for enhancement of pattern perception,not for plotting accuracy.Grids are drawn in equal intervals instead of numerical values.These reference lines are particularly powerful when we need to do scanning and matching of a matrix of scatterplots.
5.1.3Fitted Curve
In statistics,?tting means?nding a description of a data set.For example,if a data set?ts into a normal distribution,the whole data set can then be described by two numbers:its mean and standard deviation.In
Figure1:Left:A simple2D scatterplot.Middle:A scatterplot with visual reference grids.Right:A?tted curve is included in the plot.
statistics visualization,?tting means?nding a smooth curve that describes the underlying pattern.In the right panel of Figure1,a curve?t to the data is plotted;a pattern not apparent from the scatterplot before may suddenly emerge.Fitting formulas are not discussed in this paper;[Tay90,Cle93]are good references for this matter.
5.1.4Banking
The perception of the orientations of line segments can be enhanced by adjusting the aspect ratio of the graph. The aspect ratio of a graph is de?ned as the height of the data rectangle divided by the width.A line segment with an orientation of45or45is the best to convey linear properties of the curve.This technique is known as the banking to45principle[CMM93].In Figure2,the same curve is plotted in three different aspect ratios.Only
Figure2:The same curve is plotted in three different aspect ratio.The upper left one conveys more information than the other two.
the upper left panel shows both a curve on the left and a straight line on the right.The banking method is covered in[Cle93].
5.1.5Scatterplot Matrix
One of the more popular statistics mdmv visualization techniques is the scatterplot matrix which presents multiple adjacent scatterplots.Each display panel in a scatterplot matrix is identi?ed by its row and column numbers in the matrix.For example,the identity of the upper left panel of the matrix in Figure3is(1,3),and the lower right panel is(3,1).The empty diagonal panels denote the variable names.Panel(2,1)is a scatterplot of parameter against while panel(1,2)is the reverse,i.e.,versus.In a scatterplot matrix,every variate is treated identically.The
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Figure3:A scatterplot matrix displays of data with three variates,,and.
basic idea is to visually link features in one panel with features in others.The redundancy is designed to improve the effect of visual linking.The technique is further enhanced with the help of reference grids.The pattern can be detected in both horizontal and vertical directions.The concept of linking is also discussed in[BMMS91].
The idea of pairwise adjacencies of variables is also a basis for the hyperbox[AC91],hierarchical axis [MGTS90,MTS91a,MTS91b],and HyperSlice[vWvL93].Despite its popularity in mdmv visualization applica-tions,nobody knows the identity of the original inventor[Cle93].The technique was?rst presented in[CCKT83].
A variety of powerful tools using this kind of multi-panel display are presented in[Cle93].The scatterplot matrix is also implemented in XmdvTool[War94].
5.1.6Other Two Dimensional Analytical Techniques
Cleveland’s book also includes other powerful graphical techniques such as medium-difference plot,quantile-quantile plot,spread-location plot,given plot,and conditional plot;?tting tools such as loess and bisquare;and visual perception techniques such as jittering and outlier deletion.
5.2Multivariate Visualization Techniques
The scatterplot matrix uses multiple2-way displays in an effort to provide correlation information among many variates simultaneously.The techniques described in this section are,however,aimed at extending the possibilities of multivariate correlation.All the techniques,with the exception of brushing and parallel coordinates,were developed after the1987NSF workshop.All of them claim positive results with real life mdmv scienti?c data. These techniques are also aimed at presenting much larger data sets than those appropriate for the statistical visualization techniques.Today’s scienti?c data is huge;terabyte sized data will soon be common.A static scatterplot is just not big enough to display more than a few hundred data items.These techniques are broadly categorized into?ve sub-groups:
Brushing allows direct manipulation of a mdmv visualization display.Only brushing a scatterplot matrix is described.
Panel matrix involves pairwise two dimensional plots of adjacent variates.Techniques included are Hyper-Slice and hyperbox.Both of them are elaborations of the scatterplot matrix.
Iconography uses variates to determine values of parameters of small graphical objects,called icons or glyphs .Thousands of data points are represented by thousands of these icons which create a visual display characterized by varying texture patterns determined by the data.The mappings of data values to graphical parameters are usually chosen to generate texture patterns that hopefully bring insight into the data.Three iconographic techniques are described:stick ?gure icon,autoglyph,and color icons.
Hierarchical displays map a subset of variates into different hierarchical levels of the display.Hierarchical axis,dimension stacking,and worlds within worlds belong to this group.These techniques support,or at least enable,dynamic interactive analysis.
Non-Cartesian displays map data into non-Cartesian axes.They include parallel coordinates and VisDB.Parallel coordinates is the only technique that is capable of studying both multidimensional objects and multidimensional data.
5.2.1Brushing
Brushing was ?rst presented in [BC87].It is included as one of the many direct manipulation techniques in
[Cle93].There are two kinds of brushing a scatterplot matrix:labeling and enhanced https://www.doczj.com/doc/364366840.html,beling involves an interactive brush (e.g.,a mouse pointer)that causes information label(s)to pop-up for particular display item(s).In enhanced linking,the brush is an adjustable rectangle.It is used to cover a set of points in one of the panels.Figure 4shows a rectangle brush in panel (3,2).Data inside the rectangle is displayed with a “+”instead of a “
.”X
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Figure 4:Enhanced brushing with the square brush located on panel (3,2).
The same changes are applied to the corresponding data points in the other panels.By looking at different panels and comparing the vertical and horizontal extent of the brush,this enhanced linking technique provides a powerful direct manipulation tool for visual conditioning analysis.It is shown that the effect of brushing is more intense in a dynamic interactive display.In general,brushing can be added to many other mdmv visualization techniques
[War94].More applications can be found in [Cle93].
5.2.2HyperSlice
HyperSlice[vWvL93]is one of the techniques invented during the elaboration and assessment stage.Like the scatterplot matrix,it has a matrix of panels,although each individual scatterplot is replaced with color or grey shaded graphics representing a scalar function of the variates.Furthermore,panels along the diagonal show the scalar function in terms of a single variate.
HyperSlice de?nes a focal point of interest12and a set of scalar widths,where
1.Only data within the range22are displayed in the panel matrix.The rest of the data only appears if the user steers the focal point near it.Color Plate1shows the display of a HyperSlice of four variates.Like the coordinate system used in the scatterplot matrix,a HyperSlice panel is identi?ed by a
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Figure5:Navigate a?ve variate HyperSlice by dragging panel(4,2).
horizontal and a vertical coordinate.For an off-diagonal panel such that,the color shows the value of the scalar function that results from?xing the values of all variates except and to the values of the focal point, while varying and over their ranges in.The diagonal panels show a graph of the scalar function versus one variate which changes over its range in.
The most important improvement of HyperSlice over the traditional scatterplot matrix is the idea of interactively navigating in the data around a user de?ned focal point.The user changes the focal point by interacting with any of the panels,as shown in Figure5.The user moves the mouse into any panel and de?nes a direction by button down,move,and up.For example,the boldface arrow in panel(4,2)represents such an interaction.The direction of each arrow shows the motion of the focal point when the focal point is dragged in panel(4,2).Notice that the length(magnitude)of the vertical arrows across the2row,is the same as the vertical component of the arrow in(4,2).Similarly,each horizontal arrow in column4has the same length as the horizontal component of the arrow in panel(4,2).Panels solely related to1,3,and5move perpendicular to the image plan.Since the matrix is somewhat similar to an orthogonal matrix(along the grey diagonal panel),the motion on the upper left half is the mirror projection of the lower right.
Interactive data navigation is a welcome addition to direct manipulation graphics.The use of the width scalar supports the notion of multiresolution analysis,and begins to address more than two-way correlations.Changing the focal point in one panel affects two variates which in turn results in simultaneous visual changes in displays of
these variates with others.HyperSlice is an example of a successful elaboration which builds on another successful tool.
5.2.3Hyperbox
We place the discussion of hyperbox [AC91]here because it works very much like the scatterplot matrix and HyperSlice.It too involves pairwise two dimensional plots of adjacent variates,yet hyperbox is very different from the other two.
A hyperbox is a two dimensional depiction of an n -dimensional box.Figure 6shows a simple hyperbox
of
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Figure 6:Left:Hyperbox of dimension 5.Right:V ariates
and are mapped to different dimensions.The two dimensional plots of versus ,versus ,and versus are shown in grey.
dimension ?ve.An -dimensional hyperbox is made up of 2lines and 12faces .There are 5225lines
and 5512
10faces in a hyperbox of dimension ?ve.For each line in a hyperbox,there are 1other lines with the same length and orientation.The length and slope of the lines are arbitrary.Both of them can be mapped to the data variates for visualization.Lines with the same length and orientation form a direction set .In Figure 6,lines 1,2,3,4,and 5form one direction set while lines I,II,III,IV ,and V form another.Given a ?ve variate data set,,and ,each variate is mapped to one direction,as shown in the hyperbox on the right hand side of Figure 6.Each face of the hyperbox can now be used to plot data of two variates such as a scatterplot or a line plot.
To support data analysis,variates can be selected by
the hyperbox along each direction set.A hyperbox cut is similar to a discrete table selection in relational databases,except that the variates are discarded instead of selected.Suppose the data we have is time-series related and is the time variate.Then the time variate can be cut and grouped into periods analogous to a histogram.Figure 7depicts an array of hyperboxes with variate U
0-10U 10-20U 20-30U 30-40
Figure 7:An array of hyperboxes excluding dimension and is shown in different time period.
and being cut.Each hyperbox depicts all values of variates ,,and that occur for all in the range de?ned for that hyperbox.With a hyperbox,and any other panel matrix type mdmv technique,occlusion occurs when the most recent value replaces the earlier displayed data value at the same spot.This is one of the reasons why an array of hyperboxes is shown instead.An example hyperbox visualization is shown in Color Plate 2.
The design of the hyperbox requires a little practice to understand.It is a more powerful tool compared to the scatterplot matrix in the sense that it is possible to map variates to both the size and shape of each facet.It also gives scientists the option to emphasize some of the more important variates and de-emphasize others.On the other hand,it violates Cleveland’s banking to 45principle.The arbitrary setting of length and orientation make it impossible to do the right banking on all facets.This means that some of the plots may not be able to convey the right information.
The idea behind Cleveland’s reference grid in the previous discussion is to maximize the visual perception of spatial locality during data scanning and matching.Once again,the arbitrary assignment of length and orientation of each facet makes it impossible for the hyperbox display to take full advantage of this technique.
5.2.4Stick Figure Icon
Pickett and Grinstein [PG88]presented the stick ?gure icon visualization technique.It is rich in concepts,and practical development [GPW89,SGB91].The original stick icon is a ?ve stick ?gure with controllable limb angle.Figure 8depicts a family of twelve of them.This icon family is designed to display data with up to ?ve
variates.
δ
χε
βα
Figure 8:Left:A ?ve stick ?gure icon with orientation plotted according to some values.Right:A stick ?gure icon family.Each one has a body and four limbs.
Four of them can be mapped to the orientation of each limb and the ?fth can be mapped to the inclination of the body.Other variates can map to the length,thickness,or color of the limbs.An example stick ?gure icon visualization showing 7variates is shown in Color Plate 3.A two stick icon is also presented in [SGB91]to display bivariate MRI data.The notion of icon construction is to allow humans to exploit the capacity to sense and discriminate the texture in a complex image.In the bivariate data example,the stick icon successfully locates a hot spot not seen in the original MR images.The authors report that these multiline icons have been used to display as many as thirty variates.However,the ?exibility of this technique can also can be a weakness.The visual discernment of an important pattern can be highly dependent upon the selection of an appropriate mapping of the data parameters to the visual parameters.Since the number of potential mappings grows as the factorial of the number of variates,the selection process can become a bottleneck.
5.2.5Autoglyph
Beddow[Bed90]describes what he calls an autoglyph.The?rst generation of his glyph,known as Datapix,was comprised of two rectangles,a?lled arc,and a circle.A few examples are discussed in[Bed92].Based on the results of Datapix,a second generation of autoglyph,which contains only a circle and a box,was developed. Suppose we have twelve variate data.Each variate is?rst normalized and then divided into three groups based on the standard deviation function,and a group representing missing values.The high and low groups are assigned to black and grey,and the rest are white.Each variate can then be mapped into one square tile inside the rectangle glyph,as shown in Figure9.The paper also presents examples of using primary colors instead of
Figure9:Autoglyph designed for twelve variate data.
black/grey/white.Beddow[Bed90,Bed92]gives a brief analysis of the use of color in such glyphs.Autoglyph was originally designed to study the correlation among large numbers of variates.It has a very compact display and can be extended to using threshold functions other than standard deviation.Keller and Keller[KK93]do not recommend this technique for group presentation because it requires time to study.
5.2.6Color Icon
Levkowitz[Lev91]describes a color icon that merges color,shape,and texture perception for iconographic integration.Color and shape are perceptually orthogonal,and are particularly good at unmixing parameters of a mdmv display.A color icon is an area on the display to which color,shape,size,orientation,boundaries,and area subdividers can be mapped by multivariate data.Figure10shows a square shaped color icon which maps up to
Figure10:A square color icon.
six variates.Each variate is mapped to one of the six thick lines.The thin lines serve as boundaries to separate neighboring icons.
There are two different ways to paint a color icon.The?rst approach requires color shading.A color is assigned to each thick line according to the value of the mapping variate.The color of the color icon is then computed by interpolating the colors assigned to all thick lines.A second way to paint a color icon is to assign
color to each pie-shaped sub-area according to the values of the mapping variates.The ?rst approach provides better parameter blending,while the second one gives better parameter separability.Figure 11depicts the
mapping Figure 11:Mapping mdmv data with one to six variates to color icons.The value is mapped to the thick line only.of color icons from data with one to six variates.The number of variate mapping can be tripled by having each variate control one of the HSV values.Icons with different shapes (such as hexagonal)can be used in place the square shaped icon.An example of color icon visualization is shown in Color Plate 4.
5.2.7Hierarchical Axis
The conventional way to describe a three dimensional Euclidean space is by using three orthogonal axes.In hierarchical axis [MGTS90,MTS91a,MTS91b],axes are laid out horizontally in a hierarchical fashion.For example,assume we have three variable ,,and ,and the domain of each variable is 0,1,2.The Euclidean 3D space and the hierarchy axes are depicted in Figure 12.This technique depends upon a relatively coarse
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2Z Y X Figure 12:Left:Orthogonal axes.Right:Hierarchy axes.
discretization of the data.A new term speed is coined to describe the hierarchical axes,with being the fastest axis and being the slowest [MGTS90,LWW90,MTS91a].Once these axes are de?ned,a variety of potential display options can be used.
One simple example is the histogram (or histograms within histograms)plot.Given a set of data
=(0,0,1),(1,0,0),(2,0,1),we have a histogram plotted on the left hand side of Figure 13.The height of the dark grey rectangle shows the value of the dependent variate .The independent variates and are described by the horizontal hierarchical axes located at the bottom.In the middle plot,the height of the light grey rectangle is de?ned as the sum of all variables,i.e.,the dark grey rectangles,enclosed inside.In this example,the sum equals to two.The use of this histogram can be clari?ed in the last plot on the right,which includes additional data.The histogram in the middle becomes embedded inside another histogram,which shows the sum of the values of each inner histogram.The sum function is only one of the many possible options used in this system.Other possible functions are min/max and mean/standard deviation .
Z0
Figure13:Left:A simple histogram plot.Middle and Right:Histograms within histograms plots.
The hierarchical axis technique can plot as many as twenty variates in one screen.For data with a larger number of records,i.e.,larger than the number of columns of pixels on the display screen,a technique called subspace zooming is introduced.A display of multivariate data involves a series of panels(the number of panels is equal to the number of variates).Each panel displays data from two hierarchical axes,ordered from the slowest to the fastest.This can be considered as a tree structure with the panel showing the slowest axis as the root.The other panels are nodes.A subspace is a sub-tree of the root.A series of panels is a path of the tree.Only the root panel is static,and only one path is shown at a time.The panels of the other paths are hidden until the user interactively clicks the speci?c data of any non-terminal panel to select another subspace(sub-tree).
A panel matrix of histograms is also presented in[MGTS90,MTS91a,MTS91b]to gain visual perception power similar to the scatterplot matrix.It is suggested that brushing(i.e.,direct manipulation graphics)can also be implemented by mapping all or some of the variates to axes with different speeds according to selection conditions. An example visualization is shown in Color Plate5.
5.2.8Dimension Stacking
Dimension Stacking[LWW90]is a variant of hierarchical axis.In the hierarchical axis technique,each element of the fastest axis is a one dimensional histogram.In dimension stacking,each element is a two dimensional-plot. If the data has an odd number of variates,a dummy variate is added.Dimension stacking also has the?avor of iconographics[PG88].Each two dimensional-plot element forms a texture pattern for data visualization and analysis.A major advantage of dimension stacking over hierarchical axis is that no extra function or rule,such as the use of sum in our previous example,is needed to plot the data.
Assume we have a data set with four independent variates,,,and,such that the corresponding sizes of the variates are2,3,5,and6.Figure14shows two different variate mapping schemes.In the?rst example (left),and are the fastest axes while and are slower.The second rectangle(versus)contains six(two by three)smaller rectangles,each of which is a two dimensional plot of versus,like the leftmost rectangle. If there is a dependent variate available,its values can be plotted as color/grey intensity in each of the squares. Otherwise,each of the two dimensional plots can be a simple scatterplot.The second example shows a different mapping with the and axes being the fastest ones.This hierarchical display technique is also implemented in XmdvTool[War94].An example of dimension stacking visualization is shown in Color Plate6.
X Y
Z W
X
Y Z
W
Figure 14:Two different variate mapping of the same data in dimension stacking.
5.2.9Worlds Within Worlds
All mdmv visualization techniques we have discussed so far,with the exception of one of the hierarchical axis techniques,involve the generation of static objects such as texture maps.The idea behind worlds within worlds
[FB90a,FB90b]is somewhat different.Dimensions are nested together with at most three variates being shown at each level,so as to generate an interactive hierarchy of displays.The slowest three axes are represented only by a display of three orthogonal axes,as shown in Figure 15.A three dimensional power glove is used
interactively
Figure 15:Worlds within worlds.V ariate ,,and
are plotted initially.V ariate ,,and are plotted after all
previous variates are de?ned.to de?ne a position in the space de?ned by these three axes.A new set of these axes appears at this point.The glove can then be used to pick a point in this space.This continues until all variates are de?ned.At the lowest level the ?nal variate can be displayed as a surface in the innermost world as shown in Figure 15.The output is a three dimensional stereo display of virtual worlds within which users can explore their data.This dynamic data visualization technique gives a new meaning to direct manipulation graphics.Different variate mappings give different views of the data.The direct manipulation process is in fact a data retrieval https://www.doczj.com/doc/364366840.html,ers have to know what they are looking for as most of the information is not visible in the initial display.This interactive process tends to be dif?cult and tedious because there are too many possible combinations of variate mappings.A new generation of worlds within worlds,AutoVisual [BF92,BF93],adds a rule-based user interface.Now users can specify the task through the interface,and the system generates virtual worlds accordingly.An example of worlds within worlds visualization is shown in Color Plate 7.
5.2.10Parallel Coordinates
All techniques we have discussed so far are designed to do data analysis on multidimensional data .They are not really aimed at studying the geometry of multidimensional objects .Parallel coordinates [IRC87,ID87,ID90],on the other hand,can do both.
In a parallel coordinate system,the axes of a multidimensional space are de?ned as parallel vertical lines separated by a distance .A point in Cartesian coordinates corresponds to a polyline in parallel coordinates.To avoid confusion,we use lower case letters for lines,and upper case letters for points in Cartesian spaces.In parallel coordinates,we use similar conventions except we put a bar superscript on all letters.
To see how a multidimensional object is represented in parallel coordinates,consider a simple two dimensional straight line
:21
where .In Figure 16,the continuous straight line in Cartesian coordinates is sampled,and the values are
X1X2Y
X
X2
X1l
d
l -Figure 16:Left:Two dimensional Cartesian coordinates.Right:Parallel coordinates.
plotted on corresponding axes in parallel coordinates.A collection of points,
,sampled from a straight line in Cartesian coordinates,corresponds to a set of lines ,
1
112
1
X
X1X2Y (10, 2)
X1
X2Figure 17:Left:The straight line,122
10,is plotted in Cartesian coordinates.Right:the same line is plotted in parallel coordinates.The intersection point,102,is located outside the two
axes.
Z X
Y
0121
2
1
2
X Y Z 0
123T 4Figure 18:Left:Locations of two aircraft in three dimensional Cartesian coordinates at time 0.Right:The trajectory is plotted with time axis,T,in parallel coordinates.
(2,2,2)at time .They are all displayed at the same spot,yet no collision occurs.On the other hand,a parallel
coordinate plot including time ,and the coordinates
is shown on the right hand side of Figure 18.Two aircraft collide if and only if they are in the same location at the same time.That means there will be a collision in location (2,2,1)at T =2.A four dimensional intersection can be detected by searching for any overlapping dashed lines.In our example,an overlap is detected at (2,2,2,1)of the parallel coordinate plot.
To help avoid collision,parallelograms can be de?ned along with the trajectory of the aircraft.The number,size,and shape of the parallelogram are computed according to its relative velocity and locations with respect to others,as shown in Figure 19.If the two aircraft are ?ying at the same velocity,the lower right hand aircraft
must
X Y Z
T X
Z Y
t
Figure 19:Left:Con?ict parallelogram.Right:At time ,the graph shows the location of one aircraft is not entirely inside the grey area of the others,so no collision occurs.
avoid any contact with the parallelogram of the upper left hand aircraft.Like our previous example,it is almost impossible to spot the con?ict in the three dimensional Cartesian plot.In Figure19,the safety zone is indicated in grey in the parallel coordinate plot.There is a con?ict at any time if the plotting of location/time of one aircraft is entirely inside the grey area of another.
Parallel coordinates can also be used to study correlations among variates in mdmv data analysis.By spotting the locations of the intersection points,see Figures16and17,we can have a rough idea about the relationships between each pair of variates.This is one of the more promising applications of parallel coordinates in mdmv visualization.The problem with this technique is the limited space available for each parallel axis.The display can rapidly darken with even a modest amount of data.An implementation of parallel coordinates is also available in XmdvTool[War94]with brushing.
5.2.11The VisDB System
Keim et al.[KKS93,KKS94,KK94]describe a multivariate visualizationtechnique that is motivated by the desire to visualize information from a database.They use a combination of distance functions and weighting factors to visualize the relevance factor of very large multivariate data.The user issues a database query which identi?es a focal point in multidimensional space.The data is arranged using a function that represents the relevance factor of that item with respect to the focal point de?ned by the query.Each display item is represented by a single pixel whose color is also determined by the relevance factor.
Once the relevance factors of the extracted data are computed,several display methods are possible.In a normal arrangement,data is sorted and arranged such that data with the highest relevance factors(smallest distance)are centered in the middle and the rest are placed in a rectangular spiral around this region as shown in Figure20.
Figure20:Normal arrangement.
In this basic form the information is somewhat limited since the position and color of each data item is determined by the same value–the distance of the item from the focal point.The upper left display in color panel9shows this display.The overall pattern of color variation gives a general sense of how all the items in the database(or at least all that?t in the display)relate to the focal point,but it does not provide any visual insight regarding the relationships among the multiple variates.Such a comparison can be accomplished by generating additional displays that are viewed simultaneously.There is one display for each variate that contributes to the query;the position of each data item is the same as in the?rst display,but the color is based on only the distance of that variate from its value at the focal point.By comparing the color patterns of two or more variate displays, it may be possible to infer correlations of these variates with respect to the query that de?nes the focal point.See Color Plate8.
The system has a heavy database orientation.The de?nition of the distance function becomes somewhat arbitary for non-metric variates and more complex for nested queries with Boolean operators.This approach is different from all the other techniques described in that it does not display the data directly,but instead presents the data based on the computed relevance factors.
5.2.12XmdvTool
Cleveland begins and ends his book[Cle93]with the same quote,“Tools matter.”The idea is that you have to pick the right technique to visualize mdmv data.The implication is that no technique alone is powerful and?exible enough to handle all mdmv scienti?c data.Ward[War94]integrates four popular static mdmv visualization techniques,one from each of the four static sub-groups de?ned earlier,into a single analysis system,XmdvTool. The four tools included are:scatterplot(panel matrix),dimension stacking(hierarchical display),star glyph (iconography),and parallel coordinates.The original brushing[BC87],which relies heavily on mouse clicking interaction,is modi?ed into a more complicated tool with user-controlled parameters.A user can control the shape,size,boundary,position,motion,and orientation of the n-dimensional brush.Some of these parameters are customized while the others can be controlled with slider widgets.The original brushing was implemented on the scatterplot matrix only.Brushing in XmdvTool is implemented on all four options.One of the major differences compared to the original version is that the brush itself is also displayed with the brushed data.In the case of parallel coordinates,the brush is just like the grey region of Figure19,and the brushed data are the polylines inside the brush.
5.3Animation of Multidimensional Multivariate Data
Data animation is no longer considered merely a means to present results.It can also be used as an exploratory tool to look for known phenomena of the data,as well as to investigate unpredicted and signi?cant effects hidden behind the data[BG89a,EKdM93].
The development of scienti?c data animation has not been as intensive as static techniques.However, researchers have tried to use animation throughout the course of mdmv visualization development.Any of the techniques we have described can be applied to each step of times series data and then displayed as an animated sequence.Below we discuss three approaches that in some way go beyond just presenting time series of static displays–grand tour methods[BA86],Exvis on a supercomputer[SGB91],and a scalar visualization animation model[BMP91].
5.3.1Grand Tour Methods
Asimov[Asi85]created the grand tour technique to project multidimensional data onto two dimensional planes. Buja and Asimov[BA86]later developed grand tour animation methods.To quote from this paper,“Our methods are based on the simple idea of moving projection planes in high(4–10)dimensional data spaces.That is,we design1-parameter families of2-planes in-space,with the parameter being thought of as time.”That is to say, suppose we have data with1variates,we take one variate out as a time parameter for animation,and project
关于欣赏的名人名言 本文是关于名人名言的，仅供参考，如果觉得很不错，欢迎点评和分享。 关于欣赏的名人名言 1、美貌常常比酒更坏，因为它能使持有者和欣赏者双方沉醉。——齐默尔曼 2、而你欣赏我因我本性不会改，别太认真，认真怎可放开自在。——林夕 3、人生犹如一本书，愚蠢者草草翻过，聪明人细细阅读。为何如此。因为他们只能读它一次。人生无益于人类，便是无价值的。 4、路是脚踏出来的，历史是人写出来的。人的每一步行动都在书写自己的历史。——吉鸿昌 5、一个不欣赏自己的人，是难以快乐的。——三毛 6、人生的价值，即以其人对于当代所做的工作为尺度。——徐玮 7、你要欣赏自己的价值，就得给世界增添价值。——歌德 8、人生天地之间，若白驹过隙，忽然而已。人生是最伟大的宝藏，我晓得从这个宝藏中选取最珍贵的珠宝。 9、春蚕到死丝方尽，人至期颐亦不休。一息尚存须努力，留作青年好范畴。——吴玉章 10、人生应该如蜡烛一样，从顶燃到底，一直都是光明的。——
萧楚女 11、人的一生可能燃烧也可能腐朽，我不能腐朽，我愿意燃烧起来！——奥斯特洛夫斯基 12、风前有恨梅千点，沙上无人月一痕。——谭知柔《晚醉口占》 13、我给我母亲添了不少乱，但是我认为她对此颇为享受。——马克·吐温 14、恨芳菲世界，游人未赏，都付与，莺和燕。——陈亮 15、在孩子们的口头心里，母亲就是上帝的名字。——萨克雷 16、欣赏，这就是为着一件事物本身而爱好它，不为旁的理由。——达·芬奇 17、你若要喜爱你自己的价值，你就得给世界创造价值。——歌德 18、我们往往只欣赏自然，很少考虑与自然共生存。——王尔德 19、人生就象弈棋，一步失误，全盘皆输，这是令人悲哀之事；而且人生还不如弈棋，不可能再来一局，也不能悔棋。人生就是跋涉，人生就是开拓，人生就是与苦难争斗角逐；假如因我们承受苦难，能使后代免于受苦，那就是我们无上的幸福。 20、世界上的一切光荣和骄傲，都来自母亲。——高尔基 21、我之所有，我之所能，都归功于我天使般的母亲。——林肯
英美文学专业开题报告 引导语：各民族的文学中都有许多惊险、恐怖的故事,但似乎没有哪一种文学像英美文学那样不仅创作出数量众多、质量优秀的恐怖文学作品,而且还形成了一个持续发展、影响广泛的哥特传统。以下 是的为大家找到的英美文学专业开题报告。希望能够帮助到大家! 论文题目theApplicationandInnovation 一、选题的意义和研究现状 1.选题的目的、理论意义和现实意义长时期以来,人们视艾米莉?勃朗特为英国文学中的“斯芬克斯”。关于她本人和她的作品都有很多难解之谜,许多评论家从不同的角度、采用不同的方法去研究,得出了不同的结论,因而往往是旧谜刚解,新谜又出,解谜热潮似永无休止。 本文立足于欧美文学中的哥特传统研究《呼啸山庄》的创作源泉,指出艾米莉?勃朗特在主题、人物形象、环境刻画、意象及情节构造等方面都借鉴了哥特传统,同时凭借其超乎寻常的想象力,将现实 与超现实融为一体,给陈旧的形式注入了激烈情感、心理深度和新鲜 活力,达到了哥特形式与激情内容的完美统一,使《呼啸山庄》既超越了哥特体裁的“黑色浪漫主义”,又超越了维多利亚时代的“现实主义”,从而展现出独具一格、经久不衰的艺术魅力。 2.与选题相关的国内外研究和发展概况 各民族的文学中都有许多惊险、恐怖的故事,但似乎没有哪一种文学像英美文学那样不仅创作出数量众多、质量优秀的恐怖文学作品,而且还形成了一个持续发展、影响广泛的哥特传统
(Gothictradition)。哥特文学现在已经成为英美文学研究中的一个重要领域。对哥特文学的认真研究开始于20世纪二三十年代,到70年代以后,由于新的学术思潮和文学批评观念的影响,该研究出现了前所未有而且日趋高涨的热潮。 根据在国际互联网上的搜索,到2000年9月为止,英美等国的学者除发表了大量关于哥特文学的论文外,还至少出版专著达184部,其中1970年以后为126部,仅90年代就达59部,几乎占总数的三分之一。当然,近年来哥特文学研究的状况不仅在于研究成果迅速增加,更重要的是它在深度和广度方面都大为拓展,并且把哥特传统同英美乃至欧洲的历史、社会、文化和文学的总体发展结合起来。 二、研究方案 1.研究的基本内容及预期的结果(大纲)研究的基本内容:本文立足于欧美文学中的哥特传统研究《呼啸山庄》的创作源泉,指出艾米莉?勃朗特在主题、人物形象、环境刻画、意象及情节构造等方面都借鉴了哥特传统,同时凭借其超乎寻常的想象力,将现实与超现实融为一体,给陈旧的形式注入了激烈情感、心理深度和新鲜活力,达到了哥特形式与激情内容的完美统一,使《呼啸山庄》既超越了哥特体裁的“黑色浪漫主义”,又超越了维多利亚时代的“现实主义”,从而展现出独具一格、经久不衰的艺术魅力。 预期的结果(大纲): 1.ASurveyofGothic1.1DefinitionofGothic 1.2theOriginofGothicNovels
教育资料 《短歌行》 《短歌行》赏析(林庚) 曹操这一首《短歌行》是建安时代杰出的名作，它代表着人生的两面，一方面是人生的忧患，一方面是人生的欢乐。而所谓两面也就是人生的全面。整个的人生中自然含有一个生活的态度，这就具体地表现在成为《楚辞》与《诗经》传统的产儿。它一方面不失为《楚辞》中永恒的追求，一方面不失为一个平实的生活表现，因而也就为建安诗坛铺平了道路。 这首诗从“对酒当歌，人生几何”到“但为君故，沉吟至今”，充分表现着《楚辞》里的哀怨。一方面是人生的无常，一方面是永恒的渴望。而“呦呦鹿鸣”以下四句却是尽情的欢乐。你不晓得何以由哀怨这一端忽然会走到欢乐那一端去，转折得天衣无缝，仿佛本来就该是这么一回事似的。这才是真正的人生的感受。这一段如是，下一段也如是。“明明如月，何时可掇?忧从中来，不可断绝。越陌度阡，枉用相存。契阔谈宴，心念旧恩。月明星稀，乌鹊南飞。绕树三匝，何枝可依。”缠绵的情调，把你又带回更深的哀怨中去。但“山不厌高，海不厌深”，终于走入“周公吐哺，天下归心”的结论。上下两段是一个章法，但是你并不觉得重复，你只觉得卷在悲哀与欢乐的旋涡中，不知道什么时候悲哀没有了，变成欢乐，也不知道什么时候欢乐没有了，又变成悲哀，这岂不是一个整个的人生吗?把整个的人生表现在一个刹那的感觉上，又都归于一个最实在的生活上。“我有嘉宾，鼓瑟吹笙”，不正是当时的情景吗?“周公吐哺，天下归心”，不正是当时的信心吗? “青青子衿”到“鼓瑟吹笙”两段连贯之妙，古今无二。《诗经》中现成的句法一变而有了《楚辞》的精神，全在“沉吟至今”的点窜，那是“青青子衿”的更深的解释，《诗经》与《楚辞》因此才有了更深的默契，从《楚辞》又回到《诗经》，这样与《鹿鸣》之诗乃打成一片，这是一个完满的行程，也便是人生旅程的意义。“月明星稀”何以会变成“山不厌高，海不厌深”?几乎更不可解。莫非由于“明月出天山”，“海上生明月”吗?古辞说：“枯桑知天风，海水知天寒”，枯桑何以知天风，因为它高；海水何以知天寒，因为它深。唐人诗“一叶落知天下秋”，我们对于宇宙万有正应该有一个“知”字。然则既然是山，岂可不高?既然是海，岂可不深呢?“并刀如水，吴盐胜雪”，既是刀，就应该雪亮；既是盐，就应该雪白，那么就不必问山与海了。 山海之情，成为漫漫旅程的归宿，这不但是乌鹊南飞，且成为人生的思慕。山既尽其高，海既尽其深。人在其中乃有一颗赤子的心。孟子主尽性，因此养成他浩然之气。天下所以归心，我们乃不觉得是一个夸张。 .
名人名言摘抄及赏析 名人名言以其深邃的哲理、丰富的内涵为人们广泛传诵，所以，值得我们摘抄并赏析。今天为你分享的是名人名言摘抄及赏析的内容，希望你会喜欢! 1) 形成天才的决定因素应该是勤奋.;;郭沫若 2) 学到很多东西的诀窍,就是一下子不要学很多.;;洛克 3) 自己的鞋子,自己知道紧在哪里.;;西班牙 4) 不要等待运气降临，应该去努力掌握知识。;;弗兰明 5) 人不能象走兽那样活着，应该追求知识和美德。;;但丁 6) 荣誉和财富，若没有聪明才智，是很不牢靠的财产。;;德谟克里特 7) 有教养的人的遗产，比那些无知的人的财富更有价值。;;德谟克里特 8) 知识是珍贵宝石的结晶，文化是宝石放出来的光泽。;;泰戈尔 9) 知识有如人体血液一样的宝贵。人缺少了血液，身体就要衰弱，人缺少了知识，头脑就要枯竭。;;高士其 10) 与其夸大胡说，不如宣布那个聪明的、智巧的、谦逊的警句：“我不知道”。;;伽利略 11) 人的理想志向往往和他的能力成正比。;;约翰逊 12) 人所缺乏的不是才干而是志向，不是成功的能力而是勤劳的
意志。;;部尔卫 13) 勇敢坚毅真正之才智乃刚毅之志向。;;拿破仑 14) 书卷多情似故人，晨昏忧乐每相亲。;;于谦 15) 读万卷书，行万里路。;;刘彝 16) 三人行，必有我师也。择其善者而从之，其不善者而改之。;;孔子 17) 知之者不如好之者，好之者不如乐之者。;;孔子 18) 敏而好学，不耻下问。;;孔子 19) 学而不思则罔，思而不学则殆。;;孔子 20) 外韧之味，久则可厌;读书之味，愈久愈深。;;程颐 名人名言摘抄及赏析（经典篇）1) 劳于读书，逸于作文。;;元代;程端礼 2) 读书如行路，历险毋惶恐。;;《清诗铎;读书》 3) 书读百遍，其义自见。;;晋;陈寿 4) 三日不读，口生荆棘;三日不弹，手生荆棘。;;清;朱舜水 5) 业精于勤，荒于嬉;行成于思，毁于随。;;韩愈 6) 鸟欲高飞先振翅，人求上进先读书。;;李苦禅 7) 艺术是一种使我们达到真实的假想。;;毕加索 8) 思考的意思是：亲近自己。;;乌纳木诺 9) 我们破灭的希望，流产的才能，失败的事业，受了挫折的雄心，往往积聚起来叫做嫉妒。;;巴尔扎克 10) 夜空挂满了星星，月亮像一只钓鱼的小船，仿佛航行在宽阔
2014 Final Exam Study Guide for American Literature Class Plan of Final Exam 1.Best Choice Question 10% 2.True or False Question 10% 3.Definition 10% 4.Give Brief Answers to Questions 30% 5.Critical Comments 40% American Literature Review Poe’s Poetic Ideas; Major Ideas in Emerson’s “Nature”; Whitman’s Style; Formal Features of Dickinson’s Poetry; Analysis of “Miniver Cheevy” by Edwin Arlington Robinson; Comment on “Stopping by Woods on a Snowy Evening” by Robert Frost; Naturalistic reading of Sister Carrie by Theodore Dreiser; The Theme and Techniques in Eliot’s “The Waste Land”; Theme and Technique in The Great Gatsby by Fitzgerald; Comment on Hemingway’s style and theme in A Farewell to Arms; Analysis of “Dry September” by William Faulkner; Literary terms: Transcendentalism American Naturalism The Lost Generation The Jazz Age Free Verse The Iceberg Analogy
曹操《短歌行【对酒当歌】》原文、翻译、赏析译文 原文 面对美酒应该高歌，人生短促日月如梭。对酒当歌，人生几何？ 好比晨露转瞬即逝，失去的时日实在太多！譬如朝露，去日苦多。 席上歌声激昂慷慨，忧郁长久填满心窝。慨当以慷，忧思难忘。 靠什么来排解忧闷？唯有狂饮方可解脱。何以解忧？唯有杜康。 那穿着青领（周代学士的服装）的学子哟，你们令我朝夕思慕。青青子衿，悠悠我心。 正是因为你们的缘故，我一直低唱着《子衿》歌。但为君故，沉吟至今。 阳光下鹿群呦呦欢鸣，悠然自得啃食在绿坡。呦呦鹿鸣，食野之苹。 一旦四方贤才光临舍下，我将奏瑟吹笙宴请宾客。我有嘉宾，鼓瑟吹笙。 当空悬挂的皓月哟，你运转着，永不停止；明明如月，何时可掇？ 我久蓄于怀的忧愤哟，突然喷涌而出汇成长河。忧从中来，不可断绝。 远方宾客踏着田间小路，一个个屈驾前来探望我。越陌度阡，枉用相存。 彼此久别重逢谈心宴饮，争着将往日的情谊诉说。契阔谈讌，心念旧恩。 明月升起，星星闪烁，一群寻巢乌鹊向南飞去。月明星稀，乌鹊南飞。 绕树飞了三周却没敛绕树三匝，何枝
翅，哪里才有它们栖身之 所？ 可依？ 高山不辞土石才见巍 峨，大海不弃涓流才见壮阔。（比喻用人要“唯才是举”，多多益善。）山不厌高，水不厌深。 只有像周公那样礼待贤 才（周公见到贤才，吐出口 中正在咀嚼的食物，马上接 待。《史记》载周公自谓： “一沐三握发，一饭三吐哺， 犹恐失天下之贤。”），才 能使天下人心都归向我。 周公吐哺，天 赏析 曹操是汉末杰出的政治家、军事家和文学家，他雅好诗章，好作乐府歌辞，今存诗22首，全是乐府诗。曹操的乐府诗多描写他本人的政治主张和统一天下的雄心壮志。如他的《短歌行》，充分表达了诗人求贤若渴以及统一天下的壮志。 《短歌行》是政治性很强的诗作，主要是为曹操当时所实行的政治路线和政策策略服务的，但是作者将政治内容和意义完全熔铸在浓郁的抒情意境之中，全诗充分发挥了诗歌创作的特长，准确而巧妙地运用了比兴手法，寓理于情，以情感人。诗歌无论在思想内容还是在艺术上都取得了极高的成就，语言质朴，立意深远，气势充沛。这首带有建安时代"志深比长""梗概多气"的时代特色的《短歌行》，读后不觉思接千载，荡气回肠，受到强烈的感染。 对酒当歌，人生几何？ 譬如朝露，去日苦多。 慨当以慷，幽思难忘。 何以解忧，唯有杜康。 青青子衿，悠悠我心。 但为君故，沈吟至今。 呦呦鹿鸣，食野之苹。 我有嘉宾，鼓瑟吹笙。 明明如月，何时可掇？ 忧从中来，不可断绝。 越陌度阡，枉用相存。 契阔谈，心念旧恩。 月明星稀，乌鹊南飞， 绕树三匝，何枝可依？ 山不厌高，海不厌深， 周公吐哺，天下归心。 《短歌行》是汉乐府的旧题，属于《相和歌?平调曲》。这就是说它本来是一个乐曲的名称，这种乐曲怎么唱法，现在当然是不知道了。但乐府《相和歌?平调曲》中除了《短歌行》还有《长歌行》，唐代吴兢《乐府古题要解》引证古诗“长歌正激烈”，魏文帝曹丕《燕歌行》“短歌微吟不能长”和晋代傅玄《艳歌行》“咄来长歌续短歌”等句，认为“长歌”、“短
外国文学名著鉴赏期末论文院—系：数学学院 科目：外国文学名著鉴赏（期末论文）班级： 08级数学与应用数学A班 姓名：沈铁 学号： 200805050149 上课时段：周五晚十、十一节课
奋斗了，才有出路 ——读《鲁宾逊漂游记》有感小说《鲁宾逊漂游记》一直深受人们的喜爱。读完这篇小说，使我对人生应该有自己的一个奋斗历程而受益匪浅。当一个人已经处于绝境的时候，还能够满怀信心的去面对和挑战生活，实在是一种可贵的精神。他使我认识到，人无论何时何地，不管遇到多大的困难，都不能被困难所吓倒，我们要勇敢的面对困难，克服困难，始终保持一种积极向上、乐观的心态去面对。在当今社会只有努力去奋斗，才会有自己的出路！ 其实现在的很多人都是那些遇到困难就退缩，不敢勇敢的去面对它。不仅如此，现在很多人都是独生子女，很多家长视子女为掌上明珠，不要说是冒险了，就连小小的家务活也不让孩子做，天天总是说：“我的小宝贝啊，你读好书就行了，其它的爸爸妈妈做就可以了。”读书固然重要，但生活中的小事也不能忽略。想一想，在荒无人烟的孤岛上，如果你连家务活都不会做，你能在那里生存吗？读完这部著作后，我不禁反问自己：“如果我像书中的鲁宾逊那样在大海遭到风暴，我能向他那样与风暴搏斗，最后逃离荒岛得救吗？恐怕我早已经被大海所淹没；如果我漂流到孤岛，能活几天？我又能干些什么？我会劈柴吗？会打猎做饭吗？我连洗洗自己的衣服还笨手笨脚的。”我们应该学习鲁宾逊这种不怕困难的精神，无论何时何地都有坚持地活下去，哪怕只有一线希望也要坚持到底，决不能放弃！我们要像鲁宾
逊那样有志气、有毅力、爱劳动，凭自己的双手创造财富，创造奇迹，取得最后的胜利。这样的例子在我们的生活中屡见不鲜。 《史记》的作者司马迁含冤入狱，可它依然在狱中完成《史记》一书，他之所以能完成此书，靠的也是他心中那顽强的毅力，永不放弃的不断努力的精神。著名作家爱迪生从小就生活在一个贫困的家庭中，可是他从小就表现出了科学方面的天赋。长大后爱迪生着力于电灯的发明与研究，他经过了九百多次的失败，可它依然没有放弃，不断努力，最后终于在第一千次实验中取得了成功。 鲁宾逊在岛上生活了二十八年，他面对了各种各样的困难和挫折，克服了许多常人无法想象的困难，自己动手，丰衣足食，以惊人的毅力，顽强的活了下来。他自从大船失事后，找了一些木材，在岛上盖了一间房屋，为防止野兽，还在房子周围打了木桩，来到荒岛，面对着的首要的就是吃的问题，船上的东西吃完以后，鲁宾逊开始打猎，有时可能会饿肚子，一是他决定播种，几年后他终于可以吃到自己的劳动成果，其实学习也是这样，也有这样一个循序渐进的过程，现在的社会，竞争无处不在，我们要懂得只有付出才会有收获，要勇于付出，在战胜困难的同时不断取得好成绩。要知道只有付出，才会有收获。鲁宾逊在失败后总结教训，终于成果；磨粮食没有石磨，他就用木头代替；没有筛子，就用围巾。鲁宾逊在荒岛上解决了自己的生存难题，面对人生挫折，鲁宾逊的所作所为充分显示了他坚毅的性格和顽强的精神。同样我们在学习上也可以做一些创新，养成一种创新精神，把鲁宾逊在荒岛，不畏艰险，不怕失败挫折，艰苦奋斗的精
名言警句赏析 本文是关于名人名言的，仅供参考，如果觉得很不错，欢迎点评和分享。 名言警句赏析 1、高岸为谷，深谷为陵。--《诗经·十月之交》 赏析： 高地变成山谷，深谷变成山陵。据科学推测，喜马拉雅山这个地方在七千万年以前还是一片汪洋大海，沧海变桑田，比喻世事多变。也比喻事物在一定的条件下总是向它相反的方面转化。 2、工欲善其事，必先利其器。一一《论语·卫灵公》 赏析： 工匠要想做好自己的工作，必须事先磨快工具。由此可知，一个人要胜任工作，必须首先学好本领。今日读好书，来日才能大展鸿图。 3、躬自厚而薄责于人。一一《论语·卫灵公》 赏析： 多责备自己，而少责备别人。这句话说的是要严以律己，宽以待人。严格要求自己，进步就快；宽容别人，就会远离怨恨。躬自：自己。“厚”后省略了一个“责”字。责：责备，要求。 4、爱人者，人恒爱之；敬人者，人恒敬之。--《孟子。离娄下》 赏析： 爱人的人别人总是爱他，尊敬别的人别人总是尊敬他。你怎样对待别
人，别人也往往会用同样的态度对待你。要想受人敬爱，必须敬爱他人。恒：常常。 2、躬自厚而薄责于人。--《论语。卫灵公》 赏析： 多责备自己，而少责备别人。这句话说的是要严以律己，宽以待人。严格要求自己，进步就快；宽容别人，就会远离怨恨。躬自：自己。“厚”后省略了一个“责”字。责：责备，要求。 3、好而知其恶，恶而知其美。--《礼记。大学》 赏析： 对你所喜欢的人，要知道他的缺点，不可偏袒；对你所厌恶的人，要知道他的优点，不可抹杀。我们评价一个人要客观公正，不要感情用事，不要因为自己的好、恶而产生偏见。好hào：喜爱。前“恶”è：缺陷和丑恶。后“恶”wù：讨厌，憎恨。 4、人一能之，己百能之；人十能之，己千能之。--《礼记。中庸》 赏析： 别人花一分力气就能学好的，自己就花百分的力气学好它；人家花十分的力气能学好的，自己就花千分的力气学好它。这句话说明人要自强，不甘落后。有了自强的心态，必然会勤奋，“勤能补拙”，原本不够聪明的人也会聪明起来了。 5、为者常成，行者常至。--《晏子春秋内篇。杂下》 赏析：
Chapter III WEE QUESTION OF FORTUNE--FOUR-FIFTY A WEEK Once across the river and into the wholesale district, she glanced about her for some likely door at which to apply. As she contemplated the wide windows and imposing signs, she became conscious of being gazed upon and understood for what she was--a wage-seeker. She had never done this thing before, and lacked courage. To avoid a certain indefinable shame she felt at being caught spying about for a position, she quickened her steps and assumed an air of indifference supposedly common to one upon an errand. In this way she passed many manufacturing and wholesale houses without once glancing in. At last, after several blocks of walking, she felt that this would not do, and began to look about again, though without relaxing her pace. A little way on she saw a great door which, for some reason, attracted her attention. It was ornamented by a small brass sign, and seemed to be the entrance to a vast hive of six or seven floors. "Perhaps," she thought, "They may want some one," and crossed over to enter. When she came within a score of feet of the desired goal, she saw through the window a young man in a grey checked suit. That he had anything to do with the concern, she could not tell, but because he happened to be looking in her direction her weakening heart misgave her and she hurried by, too overcome with shame to enter. Over the way stood a great six- story structure, labelled Storm and King, which she viewed with rising hope. It was a wholesale dry goods concern and employed women. She could see them moving about now and then upon the upper floors. This place she decided to enter, no matter what. She crossed over and walked directly toward the entrance. As she did so, two men came out and paused in the door. A telegraph messenger in blue dashed past her and up the few steps that led to the entrance and disappeared. Several pedestrians out of the hurrying throng which filled the sidewalks passed about her as she paused, hesitating. She looked helplessly around, and then, seeing herself observed, retreated. It was too difficult a task. She could not go past them. So severe a defeat told sadly upon her nerves. Her feet carried her mechanically forward, every foot of her progress being a satisfactory portion of a flight which she gladly made. Block after block passed by. Upon streetlamps at the various corners she read names such as Madison, Monroe, La Salle, Clark, Dearborn, State, and still she went, her feet beginning to tire upon the broad stone flagging. She was pleased in part that the streets were bright and clean. The morning sun, shining down with steadily increasing warmth, made the shady side of the streets pleasantly cool. She looked at the blue sky overhead with more realisation of its charm than had ever come to her before. Her cowardice began to trouble her in a way. She turned back, resolving to hunt up Storm and King and enter. On the way, she encountered a great wholesale shoe company, through the broad plate windows of which she saw an enclosed executive
曹操《短歌行》其二翻译及赏析 引导语：曹操(155—220)，字孟德，小名阿瞒，《短歌行 二首》 是曹操以乐府古题创作的两首诗， 第一首诗表达了作者求贤若渴的心 态，第二首诗主要是曹操向内外臣僚及天下表明心迹。 短歌行 其二 曹操 周西伯昌，怀此圣德。 三分天下，而有其二。 修奉贡献，臣节不隆。 崇侯谗之，是以拘系。 后见赦原，赐之斧钺，得使征伐。 为仲尼所称，达及德行， 犹奉事殷，论叙其美。 齐桓之功，为霸之首。 九合诸侯，一匡天下。 一匡天下，不以兵车。 正而不谲，其德传称。 孔子所叹，并称夷吾，民受其恩。 赐与庙胙，命无下拜。 小白不敢尔，天威在颜咫尺。 晋文亦霸，躬奉天王。 受赐圭瓒，钜鬯彤弓， 卢弓矢千，虎贲三百人。 威服诸侯，师之所尊。 八方闻之，名亚齐桓。 翻译 姬昌受封为西伯，具有神智和美德。殷朝土地为三份，他有其中两分。 整治贡品来进奉，不失臣子的职责。只因为崇侯进谗言，而受冤拘禁。 后因为送礼而赦免， 受赐斧钺征伐的权利。 他被孔丘称赞， 品德高尚地位显。 始终臣服殷朝帝王，美名后世流传遍。齐桓公拥周建立功业，存亡继绝为霸 首。
聚合诸侯捍卫中原，匡正天下功业千秋。号令诸侯以匡周室，主要靠的不是 武力。 行为磊落不欺诈，美德流传于身后。孔子赞美齐桓公，也称赞管仲。 百姓深受恩惠，天子赐肉与桓公，命其无拜来接受。桓公称小白不敢，天子 威严就在咫尺前。 晋文公继承来称霸，亲身尊奉周天王。周天子赏赐丰厚，仪式隆重。 接受玉器和美酒，弓矢武士三百名。晋文公声望镇诸侯，从其风者受尊重。 威名八方全传遍，名声仅次于齐桓公。佯称周王巡狩，招其天子到河阳，因 此大众议论纷纷。 赏析 《短歌行》 (“周西伯昌”)主要是曹操向内外臣僚及天下表明心 迹，当他翦灭群凶之际，功高震主之时，正所谓“君子终日乾乾，夕惕若 厉”者，但东吴孙权却瞅准时机竟上表大说天命而称臣，意在促曹操代汉 而使其失去“挟天子以令诸侯”之号召， 故曹操机敏地认识到“ 是儿欲据吾著炉上郁!”故曹操运筹谋略而赋此《短歌行 ·周西伯 昌》。 西伯姬昌在纣朝三分天下有其二的大好形势下， 犹能奉事殷纣， 故孔子盛称 “周之德， 其可谓至德也已矣。 ”但纣王亲信崇侯虎仍不免在纣王前 还要谗毁文王，并拘系于羑里。曹操举此史实，意在表明自己正在克心效法先圣 西伯姬昌，并肯定他的所作所为，谨慎惕惧，向来无愧于献帝之所赏。 并大谈西伯姬昌、齐桓公、晋文公皆曾受命“专使征伐”。而当 今天下时势与当年的西伯、齐桓、晋文之际颇相类似，天子如命他“专使 征伐”以讨不臣，乃英明之举。但他亦效西伯之德，重齐桓之功，戒晋文 之诈。然故作谦恭之辞耳，又谁知岂无更讨封赏之意乎 ?不然建安十八年(公元 213 年)五月献帝下诏曰《册魏公九锡文》，其文曰“朕闻先王并建明德， 胙之以土，分之以民，崇其宠章，备其礼物，所以藩卫王室、左右厥世也。其在 周成，管、蔡不静，惩难念功，乃使邵康公赐齐太公履，东至于海，西至于河， 南至于穆陵，北至于无棣，五侯九伯，实得征之。 世祚太师，以表东海。爰及襄王，亦有楚人不供王职，又命晋文登为侯伯， 锡以二辂、虎贲、斧钺、禾巨 鬯、弓矢，大启南阳，世作盟主。故周室之不坏， 系二国是赖。”又“今以冀州之河东、河内、魏郡、赵国、中山、常 山，巨鹿、安平、甘陵、平原凡十郡，封君为魏公。锡君玄土，苴以白茅，爰契 尔龟。”又“加君九锡，其敬听朕命。” 观汉献帝下诏《册魏公九锡文》全篇，尽叙其功，以为其功高于伊、周，而 其奖却低于齐、晋，故赐爵赐土，又加九锡，奖励空前。但曹操被奖愈高，心内 愈忧。故曹操在曾早在五十六岁写的《让县自明本志令》中谓“或者人见 孤强盛， 又性不信天命之事， 恐私心相评， 言有不逊之志， 妄相忖度， 每用耿耿。
（一）文学常识 一、古希腊罗马 1．（1）宙斯(罗马神话称为朱庇特)，希腊神话中最高的天神，掌管雷电云雨，是人和神的主宰。 （2）阿波罗，希腊神话中宙斯的儿子，主管光明、青春、音乐、诗歌等，常以手持弓箭的少年形象出现。 （3）雅典那，希腊神话中的智慧女神，雅典城邦的保护神。 （4）潘多拉，希腊神话中的第一个女人，貌美性诈。私自打开了宙斯送她的一只盒子，里面装的疾病、疯狂、罪恶、嫉妒等祸患，一齐飞出，只有希望留在盒底，人间因此充满灾难。“潘多拉的盒子”成为“祸灾的来源”的同义语。 （5）普罗米修斯，希腊神话中造福人间的神。盗取天火带到人间，并传授给人类多种手艺，触怒宙斯，被锁在高加索山崖，受神鹰啄食，是一个反抗强暴、不惜为人类牺牲一切的英雄。 （6）斯芬克司，希腊神话中的狮身女怪。常叫过路行人猜谜，猜不出即将行人杀害；后因谜底被俄底浦斯道破，即自杀。后常喻“谜”一样的人物。与埃及狮身人面像同名。 2．荷马，古希腊盲诗人。主要作品有《伊利亚特》和《奥德赛》，被称为荷马史诗。《伊利亚特》叙述十年特洛伊战争。《奥德赛》写特洛伊战争结束后，希腊英雄奥德赛历险回乡的故事。马克思称赞它“显示出永久的魅力”。 3．埃斯库罗斯，古希腊悲剧之父，代表作《被缚的普罗米修斯》。6．阿里斯托芬，古希腊“喜剧之父”代表作《阿卡奈人》。 4．索福克勒斯，古希腊重要悲剧作家，代表作《俄狄浦斯王》。5．欧里庇得斯，古希腊重要悲剧作家，代表作《美狄亚》。 二、中世纪文学 但丁，意大利人，伟大诗人，文艺复兴的先驱。恩格斯称他是“中世纪的最后一位诗人，同时又是新时代的最初一位诗人”。主要作品有叙事长诗《神曲》，由地狱、炼狱、天堂三部分组成。《神曲》以幻想形式，写但丁迷路，被人导引神游三界。在地狱中见到贪官污吏等受着惩罚，在净界中见到贪色贪财等较轻罪人，在天堂里见到殉道者等高贵的灵魂。 三、文艺复兴时期 1．薄迦丘意大利人短篇小说家，著有《十日谈》拉伯雷，法国人，著《巨人传》塞万提斯，西班牙人,著《堂?吉诃德》。 2．莎士比亚，16-17世纪文艺复兴时期英国伟大的剧作家和诗人，主要作品有四大悲剧——《哈姆雷特》、《奥赛罗》《麦克白》、《李尔王》，另有悲剧《罗密欧与朱丽叶》等，喜剧有《威尼斯商人》《第十二夜》《皆大欢喜》等，历史剧有《理查二世》、《亨利四世》等。马克思称之为“人类最伟大的戏剧天才”。 四、17世纪古典主义 9．笛福，17-18世纪英国著名小说家，被誉为“英国和欧洲小说之父”，主要作品《鲁滨逊漂流记》，是英国第一部现实主义长篇小说。10．弥尔顿，17世纪英国诗人，代表作：长诗《失乐园》，《失乐园》，表现了资产阶级清教徒的革命理想和英雄气概。 25．拉伯雷，16世纪法国作家，代表作：长篇小说《巨人传》。 26．莫里哀，法国17世纪古典主义文学最重要的作家，法国古典主义喜剧的创建者，主要作品为《伪君子》《悭吝人》（主人公叫阿巴公）等喜剧。 五、18世纪启蒙运动 1）歌德，德国文学最高成就的代表者。主要作品有书信体小说《少年维特之烦恼》，诗剧《浮士德》。 11．斯威夫特，18世纪英国作家，代表作：《格列佛游记》，以荒诞的情节讽刺了英国现实。 12．亨利·菲尔丁，18世纪英国作家，代表作：《汤姆·琼斯》。 六、19世纪浪漫主义 （1拜伦， 19世纪初期英国伟大的浪漫主义诗人，代表作为诗体小说《唐璜》通过青年贵族唐璜的种种经历，抨击欧洲反动的封建势力。《恰尔德。哈洛尔游记》 （2雨果，伟大作家，欧洲19世纪浪漫主义文学最卓越的代表。主要作品有长篇小说《巴黎圣母院》、《悲惨世界》、《笑面人》、《九三年》等。《悲惨世界》写的是失业短工冉阿让因偷吃一片面包被抓进监狱，后改名换姓，当上企业主和市长，但终不能摆脱迫害的故事。《巴黎圣母院》 弃儿伽西莫多，在一个偶然的场合被副主教克洛德.孚罗洛收养为义子，长大后有让他当上了巴黎圣母院的敲钟人。他虽然十分丑陋而且有多种残疾，心灵却异常高尚纯洁。 长年流浪街头的波希米亚姑娘拉.爱斯梅拉达，能歌善舞，天真貌美而心地淳厚。青年贫诗人尔比埃尔.甘果瓦偶然同她相遇，并在一个更偶然的场合成了她名义上的丈夫。很有名望的副教主本来一向专心于"圣职"，忽然有一天欣赏到波希米亚姑娘的歌舞，忧千方百计要把她据为己有，对她进行了种种威胁甚至陷害，同时还为此不惜玩弄卑鄙手段，去欺骗利用他的义子伽西莫多和学生甘果瓦。眼看无论如何也实现不了占有爱斯梅拉达的罪恶企图，最后竟亲手把那可爱的少女送上了绞刑架。 另一方面，伽西莫多私下也爱慕着波希米亚姑娘。她遭到陷害，被伽西莫多巧计救出，在圣母院一间密室里避难，敲钟人用十分纯朴和真诚的感情去安慰她，保护她。当她再次处于危急中时，敲钟人为了援助她，表现出非凡的英勇和机智。而当他无意中发现自己的"义父"和"恩人"远望着高挂在绞刑架上的波希米亚姑娘而发出恶魔般的狞笑时，伽西莫多立即对那个伪善者下了最后的判决，亲手把克洛德.孚罗洛从高耸入云的钟塔上推下，使他摔的粉身碎骨。 （3司汤达，批判现实主义作家。代表作《红与黑》，写的是不满封建制度的平民青年于连，千方百计向上爬，最终被送上断头台的故事。“红”是将军服色，指“入军界”的道路；“黑”是主教服色，指当神父、主教的道路。 14．雪莱，19世纪积极浪漫主义诗人，欧洲文学史上最早歌颂空想社会主义的诗人之一，主要作品为诗剧《解放了的普罗米修斯》，抒情诗《西风颂》等。 15．托马斯·哈代，19世纪英国作家，代表作：长篇小说《德伯家的苔丝》。 16．萨克雷，19世纪英国作家，代表作：《名利场》 17．盖斯凯尔夫人，19世纪英国作家，代表作：《玛丽·巴顿》。 18．夏洛蒂?勃朗特，19世纪英国女作家，代表作：长篇小说《简?爱》19艾米丽?勃朗特，19世纪英国女作家，夏洛蒂?勃朗特之妹，代表作：长篇小说《呼啸山庄》。 20．狄更斯，19世纪英国批判现实主义文学的重要代表，主要作品为长篇小说《大卫?科波菲尔》、《艰难时世》《双城记》《雾都孤儿》。21．柯南道尔，19世纪英国著名侦探小说家，代表作品侦探小说集《福尔摩斯探案》是世界上最著名的侦探小说。 七、19世纪现实主义 1、巴尔扎克，19世纪上半叶法国和欧洲批判现实主义文学的杰出代表。主要作品有《人间喜剧》，包括《高老头》、《欧也妮·葛朗台》、《贝姨》、《邦斯舅舅》等。《人间喜剧》是世界文学中规模最宏伟的创作之一，也是人类思维劳动最辉煌的成果之一。马克思称其“提供了一部法国社会特别是巴黎上流社会的卓越的现实主义历史”。
1、锲而不舍，金石可镂。 2、立志、工作、，是人类活动的三大要素。——巴斯德 3、有很多人是用青春的幸福作成功代价的。——莫扎特 4、我们手里的金钱是保持自由的一种工具。——卢梭 5、世上有很多好东西，是“带不走”的。 6、建筑在别人痛苦上的幸福不是真正的幸福。——阿·巴巴耶娃 7、一切幸福都并非没有烦恼，而一切逆境也绝非没有希望。——培根 8、灵感不过是“顽强的劳动而获得的奖赏”。——列宾 9、除了无法达成心愿之外，就数心愿达成了最伤感。 10、成功不是将来才有的，而是从决定去做的那一刻起，持续累积而成。 11、无论何时，不管怎样，我也绝不允许自己有一点点心丧气。——爱迪生 12、古往今来，凡成就事业，对人类有所作为的，无不是脚踏实地，艰苦登攀的结果。——钱三强 13、生活真象这杯浓酒，不经三番五次的提炼呵，就不会这样可口！——郭小川 14、失败是块磨刀石。 15、每一种或不利的突变，是带着同样或较大的有利的种子。——爱默生 16、我死国生，我死犹荣，身虽死精神长生，成功成仁，实现大同。——赵博生 17、一个人的价值，应该看他贡献什么，而不应当看他取得什么。——爱因斯坦 18、平凡的脚步也可以走完伟大的行程。 19、伟人之所以伟大，是因为他与别人共处逆境时，别人失去了信心，他却下决心实现自己的目标。 20、智慧源于勤奋，伟大出自平凡。——民谚 21、冬天已经到来，春天还会远吗？——雪莱
22、百败而其志不折。 23、如烟往事俱忘却，心底无私天地宽。——陶铸 24、沉沉的黑夜都是白天的前奏。——郭小川 25、世上最精明的糊涂便是“忘记”。 26、你明白，人的一生，既不是人们想象的那么好，也不是那么坏。——莫泊桑 27、爱，如呼吸。深爱，就是深呼吸。 28、莫找借口失败，只找理由成功。（不为失败找理由，要为成功找方法） 29、要做的事情总找得出时间和机会；不愿意做的事情也总能找得出借口。 30、壮志与毅力是事业的双翼--歌德大自然既然在人间造成不同程度的强弱，也常用破釜沉舟的斗争，使弱者不亚于强者。——孟德斯鸠 31、卓越的人一大优点是：在不利与艰难的遭遇里百折不饶。——贝多芬 32、天才就是这样，终身劳动，便成天才。——门捷列夫 33、天才就是无止境刻苦勤奋的能力。——卡莱尔 34、我以为挫折、磨难是锻炼意志、增强能力的好机会。——邹韬奋 35、首先是最崇高的思想，其次才是金钱；光有金钱而没有最崇高的思想的社会是会崩溃的。——陀思妥耶夫斯基 36、耐心之树，结黄金之果。 37、如果我们想要更多的玫瑰花，就必须种植更多的玫瑰树。 38、忍耐和是痛苦的，但它会逐给你好处。 39、骆驼走得慢，但终能走到目的地。 40、勤能补拙是良训，一分辛劳一分才。——华罗庚 41、严肃的人的幸福，并不在于风流、娱乐与欢笑这种种轻佻的伴侣，而在于坚忍与刚毅。——西塞罗 42、伟大的作品，不是靠力量而是靠坚持才完成的。
目的。笔者曾进行了这方面的一个实验；对乐理、视唱程度相当的Ａ、Ｂ两个班，在讲小调式时，Ａ班此时视唱只唱小调式作品，边唱边分析；Ｂ班只在乐理课上选一些典型作品作简单分析，而视唱课对小调作品概不介绍，结果发现Ａ班大部分同学对小调式的特点掌握清晰、分析作品透彻，而Ｂ班多数同学对小调式作品的分析仍感糊涂。 （二）乐理和视唱由同一老师任教，选用乐理与视唱进度相当、联系密切的教材。 两门课由同一老师任教，这样就使老师对两门课的进度有更好的把握，从而做到在教学内容上的衔接，使乐理教学与视唱教学融为一体。现在有些院校已开始了这方面的尝试，如河南的黄河科技学院，商丘师院等。据笔者了解，由同一个老师任教班级学生的乐理视唱学习效果明显好于非同一教师任教的班级。 教材的选择对教学影响极大，原来的视唱教材极少有乐理内容，而乐理教材中的谱例也不多。目前新的教材已改变了这种状况，现在视唱教材中有了很好的理论知识讲授，如许敬行、孙虹编著，高教出版社出版的《视唱练习》。乐理教材有了更多的谱例，如贾方爵编著、 西南师大出版社出版的《基本乐理》。选用此种联系密切的教材， 教师在教学的过程中更易做到理论联系实际，学生在自学时也更易理解。 （三）加强乐理、视唱教学与其他科目的横向联系 音乐中的许多课程，如欣赏、民族民间音乐、合唱、音乐史等的学习对学生综合专业素质的提高有很大帮助，对它们的学习与对乐理和视唱的学习又能达到互相促进的目的。在乐理学教学中，学生最易迷惑的就是调式调性的判断，而进行调式判断时首先要靠听觉分辨作品的五声性和西方性。但如何能够分辨其是五声性作品还是西方调式体系作品，靠的是学生对音乐的基本鉴赏力。这就要求学生平时在欣赏课上要认真去听、去辨别，而欣赏老师也应该对不同体裁的音乐作品进行详细的讲解，并引导学生去辨别。经过这样长期的训练，学生对于不同地域、不同风格的音乐作品都有了了解和掌握，辨别调式的问题自然迎刃而解。同样，民族民间课程的学习对于学生了解和掌握各民族的音乐风格、特点起重要作用。经过同学们对民歌、戏曲等曲型片断的实际训练，在视唱这些有特点的作品时，其音乐风格的把握就会更好一些，因律制原因产生的音准问题也会大大减少，学生对民族风格的视唱会唱得更有味，把握得更准确。在这方面，如果乐理和视唱课需要，可会同其他科目的教师对教学进度、教学内容作一些适当的调整，使之与乐理、视唱教学相适应。如有必要，也可请这方面的专家，就某一问题进行一次讲座，以加深同学们对某些问题的理解。教师亦可有目的地向学生介绍一些此方面的书籍，让学生去阅读；介绍一些音响资料，让学生有目的地听，从而拓宽学生的知识面，进而更好地学习和掌握乐理和视唱。 总之，视唱与乐理相结合并密切联系相关科目的教学方法，不仅可以使学生增加对音乐理论的感性认识，加强视唱时的理论指导，更增加了视唱课的趣味性，使乐理不再枯燥，视唱课不再单调。 当然，加强乐理、视唱与其他科目教学的联系并不是说这些课程什么地方都可以互融。有些乐理知识如音律很难让学生唱出来。但这并不能否认加强视唱、乐理与其他学科教学联系的必要性。 作者简介：刘建坤，商丘师范学院音乐系教师。 一、美国自然主义的产生 内战之后，美国处于一段相对和平的时期，资本主义经济蓬勃发展。然而，在经济发展的背后，人们却普遍产生了一种悲观情绪，传统的理想主义被抛弃，宇宙的在规律性和机械性中蕴涵的漠然性使其变成了人类的敌人，至少已经不是人类慈祥的朋友。斯蒂芬?克莱恩（Ｓｔｅｐｈｅｎ Ｃｒａｎｅ（１８７１－１９００））的一首小诗Ｇｏｄ ｉｓ ｃｏｌｄ［１］ 便生动地反映了这种状况： 一个人对宇宙说：／“阁下，我存在的！”／“不过，”传来宇宙的回答，／“你的存在虽是事实，却／并没有使我产生义务感。” 对这一思潮起决定性作用的是达尔文的进化论（Ｔｈｅｏｒｙ ｏｆ Ｅｖｏｌｕｔｉｏｎ）。达尔文认为：影响生物进化的因素有三种，即：自然选择、性的选择及个体有生之期获得特性的遗传。进化论的诞生，是对传统神学及理想主义神学的全盘否定，它取消了上帝设计师和创造者的地位，进而强调人类产生过程的机械性，以及人类进化过程的因果循环性。受其影响，悲观、忧郁的新自然哲学应运而生。新自然哲学指出，自然是一座对人类遭遇无动于衷的庞大机器，人类在自然中必然要为生存而相互竞争，而且，部分人的毁灭是人类进步中不可避免的现象。 悲观的新自然哲学在文学中的反映就是产生悲观宿命的自然主义文学。英国哲学家赫伯特?斯宾塞（ＨｅｒｂｅｒｔＳｐｅｎｃｅｒ）的“社会达尔文主义” （Ｓｏｃｉａｌ Ｄａｒｗｉｎｉｓｍ）以及美国内战后的社会状况，对自然主义在美国的产生和发展起了极大的作用。当时的主要作家往往把人置于庞大的自然和社会背景中，从而显示其渺小、脆弱以及无可奈何。 ＂生存＂是人类活动的最高目标，道德规范对于实际生活已经毫无意义。 在美国自然主义文学中，最为突出的代表是西奥多?德莱塞。 二、德莱塞在美国文学史上的地位 西奥多?德莱塞 ［Ｔｈｅｏｄｏｒｅ Ｄｒｅｉｓｅｒ （１８７１－１９４５）］是美国现代小说的先驱和代表作家，被认为是同海明威、福《嘉莉妹妹》是美国自然主义文学大师西奥多?德莱塞的处女作和成名作。自然主义在书中 主要表现为作者对失败者的深刻同情。 《嘉莉妹妹》中 自然主义赏析 文／姚晓鸣 编辑：冯彬彬 69 美与时代 ２００３．１１下